Top AI Engineer Jobs in Singapore

Early-career TechOps role: build dashboards/alerts, script repetitive ticket steps, participate in RCAs, log user pain points and propose fixes, create a lightweight agent (e.g., Telegram bot) with reusable skills, optimize incident-management workflows, and write actionable runbooks to improve support-driven user experience.

Design, develop, and deploy agentic AI solutions to optimize network automation, predictive maintenance, anomaly detection, and intelligent network management. Collaborate with networking stakeholders to translate goals into technical requirements, ensure compliance with performance, risk, and regulatory standards, research emerging agentic AI trends, and provide troubleshooting, QA, and documentation for production AI systems.

Lead technical design and delivery of core products across backend, frontend, APIs, databases and infrastructure. Hands-on coding, system design, code reviews, production debugging, mentorship, and driving engineering initiatives. Ensure reliability, observability, security, and scalable architectures while collaborating with product, design and AI teams to ship high-quality features.

Lead technical design and execution of major products and systems, write and review code, make architecture decisions, mentor engineers, drive initiatives from design to production, own reliability, testing, security and observability, and collaborate with product, design and AI teams to deliver scalable, maintainable systems.

Technical lead responsible for designing and shipping scalable backend and frontend systems, making architecture decisions, writing and reviewing code, mentoring engineers, improving reliability, tooling and observability, and owning production incidents from diagnosis to resolution while collaborating with product, design and AI teams.

Lead technical design and execution of a scalable AI-native email product. Hands-on coding, system design across backend/frontend/APIs/databases/cloud, code review, production debugging, mentoring engineers, improving tooling, and owning reliability from design through incidents.

Design, build, deploy and maintain scalable AI/ML applications, LLM services and data pipelines. Collaborate with data scientists and product engineers, ensure performant, reliable, and well-tested code, influence roadmap, and provide technical leadership and mentoring.

Design, build, and deploy AI-enabled products (RAG, retrieval, reasoning) and supporting web systems and data pipelines. Integrate emerging AI technologies, prototype rapidly, collaborate with cross-functional teams, and ensure robust, scalable, and secure software solutions that meet analyst needs.

Develop and maintain scalable backend systems, implement APIs, manage databases, and collaborate with stakeholders to deliver AI solutions.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Design and build AI-powered developer platforms, agent orchestration, RAG/retrieval systems, and CI/CD-integrated workflows to automate code generation, testing, deployment, and engineering productivity. Drive AI adoption, define best practices, and measure impact across Group Wealth Engineering.

Design, develop, and evaluate AI-driven solutions for sustainable living. Collect and preprocess multimodal datasets, build data pipelines and experiments, analyze model performance, iterate on models, and collaborate on publications, reports, and demonstrations.

Develop, train, fine-tune and evaluate multilingual and multimodal LLMs; build scalable multilingual text data pipelines; support large-scale distributed training, experiment tracking and compute optimisation; reproduce and translate research into production improvements; create reusable training/evaluation assets and deployment-ready components; collaborate with academic and industry partners and support community outreach.

Build and maintain end-to-end modules for a multimodal data platform, from frontend UI/UX to backend services and cloud deployment. Implement tooling for evaluations, benchmarks, and data annotation; integrate evaluation frameworks; set up CI/CD, monitoring, and cost-effective cloud infrastructure; collaborate with cross-functional teams.

Operate and maintain the Data Platform across GCP and AWS, ensuring reliability, security, cost and performance. Manage IaC and CI/CD, run incident response and post-mortems, operate central data management (pipelines, storage, catalogues, access controls), improve observability and automation, and apply AI tools to streamline ops and tooling.

Lead design and delivery of complex, scalable software using AI-assisted development. Mentor engineers, implement CI/CD and DevOps practices, establish AI/GenAI best practices and guardrails, validate AI-generated outputs, and contribute to reusable accelerators, presales, and Gen-E2 methodology.

Design, build, and maintain production-grade full‑stack applications using modern frameworks. Implement CI/CD and testing, ensure code quality and security, and use generative AI tools (e.g., Copilot) and prompt engineering as core parts of the development workflow. Validate AI outputs, collaborate with product teams, and contribute to internal Gen-E2 practices and playbooks.

Design and build secure integrations between a Digital Twin platform and enterprise systems (CMMS, BMS, SCADA, IoT). Implement REST APIs, automation pipelines, and Model Context Protocol tools to enable human-supervised AI agent workflows with audit trails, permissions, and safeguards. Collaborate with architects, product, and FM partners to translate requirements into scalable, secure integrations.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Lead development and deployment of agentic AI and RAG solutions for network automation, predictive maintenance, anomaly detection, and intelligent network management. Architect scalable network AI systems, drive AI strategy with networking teams, mentor engineers, implement MLOps/CI-CD, and ensure performance, governance, and regulatory compliance.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

As a Senior AI Engineer, you will utilize AI engineering and delivery expertise to drive transformative projects for clients while building relationships and expanding your knowledge.